Accelerating protein engineering: an integrated framework combines protein language models and epistatic landscape modeling
In a recent study published in Science , Tran et al. introduced MULTI-evolve (model guided, universal, targeted installation of multimutants), a machine learning-guided directed evolution (MLDE) framework that rapidly designs hyperactive multimutant proteins. 1 By integrating protein language models (PLMs), epistasis-aware modeling, and a high-ef fi ciency multisite mutagenesis platform, the authors address the major combinatorial challenge that has long bottlenecked protein engineering and therapeutic development. Protein function is encoded by amino acid sequence, yet discovering productive mutational combinations remains dif fi cult because protein fi tness landscapes are high-dimensional and strongly shaped by epistatic interactions. Directed evolution has long provided a powerful route for protein optimization through repeated mutagenesis and screening. 2 More recently, MLDE has expanded accessible sequence space through in silico screening, but its performance still depends on accurately capturing epistatic interactions, the context-dependent effects between mutations that shape protein fi tness. 3 In particular, many existing approaches require large training datasets, multiple experimental rounds, or labor-intensive synthesis, and remain limited in extrapolating to high-order multimutants because of incomplete epistatic modeling. Tran et al. address these limitations with an end-to-end framework for direct multimutant exploration. MULTI-evolve is built on three conceptual components. First, the framework deploys a PLM zero-shot ensemble approach to nominate function-enhancing single mutations. By combining structure-informed and sequence-based models with z-score normalization, the authors improved the identi fi cation of productive mutations compared with individual PLMs alone. Second, they trained fully connected neural networks (FCNNs) on a compact dataset of experimentally characterized single and double mutants to learn epistatic interactions and predict higher-order variants. This data-ef